Various factors are believed to govern the selection of references in
citation networks, but a precise, quantitative determination of their
importance has remained elusive. In this paper, we show that three factors can
account for the referencing pattern of citation networks for two topics, namely
"graphenes" and "complex networks", thus allowing one to reproduce the
topological features of the networks built with papers being the nodes and the
edges established by citations. The most relevant factor was content
similarity, while the other two - in-degree (i.e. citation counts) and {age of
publication} had varying importance depending on the topic studied. This
dependence indicates that additional factors could play a role. Indeed, by
intuition one should expect the reputation (or visibility) of authors and/or
institutions to affect the referencing pattern, and this is only indirectly
considered via the in-degree that should correlate with such reputation.
Because information on reputation is not readily available, we simulated its
effect on artificial citation networks considering two communities with
distinct fitness (visibility) parameters. One community was assumed to have
twice the fitness value of the other, which amounts to a double probability for
a paper being cited. While the h-index for authors in the community with larger
fitness evolved with time with slightly higher values than for the control
network (no fitness considered), a drastic effect was noted for the community
with smaller fitness
